A Theoretical Development and Analysis of Jumping Gene Genetic Algorithm.
ABSTRACT Recently, gene transpositions have gained their power andattentionsincomputationalevolutionaryalgorithmdesigns.In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first pro- posedandtwonewgenetranspositionoperations,namely,cut-and- paste and copy-and-paste, were introduced. Although the outper- formance of JGGA has been demonstrated by some detailed statis- tical analyses based on numerical simulations, more rigorous the- oretical justification is still in vain. In this paper, a mathematical model based on schema is derived. It then provides theoretical jus- tifications on why JGGA is superiority in searching, particularly when it is applied to solve multiobjective optimization problems. The studies are also further verified by solving some optimization problems and comparisons are made between different optimiza- tion algorithms.
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ABSTRACT: Exploration and exploitation are two cornerstones of evolutionary multiobjective optimization. Most of the existing works pay more attention to the exploitation, which mainly focuses on the fitness assignment and environmental selection. However, the exploration, usually realized by traditional genetic search operators, such as crossover and mutation, has not been fully addressed yet. In this paper, we propose a general learning paradigm based on Jumping Genes (JG) to enhance the exploration ability of multiobjective evolutionary algorithms. This paradigm adapts the JG to the continuous search space, and its activation is completely adaptive during the evolutionary process. Moreover, in order to efficiently utilize the useful information, only non-dominated solutions eliminated by the environmental selection are chosen for the secondary exploitation. Empirical studies demonstrate that the performance of a baseline algorithm can be significantly improved by the proposed paradigm.Information Sciences 03/2013; 226:1-22. · 3.64 Impact Factor
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ABSTRACT: This paper provides an overview of optimization algorithms for antennas and radio frequency (RF)/microwave circuit designs. The significance of wireless communication to our daily lives and industrial engineering will first be discussed. Antennas and circuits used in wireless communication will then be introduced followed by a discussion on the need for optimization. After that, this paper will focus on three widely used optimization algorithms for antennas and RF/microwave circuit design. A survey of the optimization of antennas and microwave devices available in the literature will be presented and will include the use of other optimization algorithms. For illustration purposes two optimization examples with measurement results will be given.IEEE Transactions on Industrial Informatics 01/2012; 8(2):216-227. · 3.38 Impact Factor
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ABSTRACT: To automate assembly planning for complex products such as aircraft components, an assembly planning and simulation system called AutoAssem has been developed. In this paper, its system architecture is presented; the main components and the key technologies in each component are discussed. The core functions of the system that have been focused include Digital Assembly Modeling, Assembly Sequence Planning (ASP), Path Planning, Visualization, and Simulation. In contrast to existing assembly planning systems, one of the novelties of the system is it allows the assembly plans be automatically generated from a CAD assembly model with minimal manual interventions. Within the system, new methodologies have been developed to: (i) create Assembly Relationship Matrices; (ii) plan assembly sequences; (iii) generate assembly paths; and (iv) visualize and simulate assembly plans. To illustrate the application of the system, the assembly of a worm gear reducer is used as an example throughout this paper for demonstration purpose. AutoAssem has been successfully applied to virtual assembly design for various complex products so far.IEEE Transactions on Industrial Informatics 01/2012; 8(3):669-678. · 3.38 Impact Factor